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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import weiboPredict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from weiboPredict import framework as fw"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.loadData()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>forward_count</th>\n",
        "      <th>comment_count</th>\n",
        "      <th>like_count</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 1626750.000000</td>\n",
        "      <td> 1626750.000000</td>\n",
        "      <td> 1626750.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td>       2.306879</td>\n",
        "      <td>       1.152022</td>\n",
        "      <td>       1.214953</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td>      92.491569</td>\n",
        "      <td>      34.765826</td>\n",
        "      <td>      22.399237</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "      <td>       0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td>   81851.000000</td>\n",
        "      <td>   35019.000000</td>\n",
        "      <td>    7020.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "        forward_count   comment_count      like_count\n",
        "count  1626750.000000  1626750.000000  1626750.000000\n",
        "mean         2.306879        1.152022        1.214953\n",
        "std         92.491569       34.765826       22.399237\n",
        "min          0.000000        0.000000        0.000000\n",
        "25%          0.000000        0.000000        0.000000\n",
        "50%          0.000000        0.000000        0.000000\n",
        "75%          0.000000        0.000000        0.000000\n",
        "max      81851.000000    35019.000000     7020.000000"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_predict_data.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>uid</th>\n",
        "      <th>mid</th>\n",
        "      <th>time</th>\n",
        "      <th>context</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>                           275331</td>\n",
        "      <td>                           275331</td>\n",
        "      <td>     275331</td>\n",
        "      <td>                                            275331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unique</th>\n",
        "      <td>                            24818</td>\n",
        "      <td>                           275331</td>\n",
        "      <td>         32</td>\n",
        "      <td>                                            228038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>top</th>\n",
        "      <td> 875a4a77b339d93f819e2c4de5bd0b57</td>\n",
        "      <td> c2a882d8859f09387be2cfb5717a744c</td>\n",
        "      <td> 2015-01-01</td>\n",
        "      <td> \u8ba9\u4f60\u4ece\u6b64\u6f6e\u8d77\u6765\uff01[\u9177]\u6700\u8fd1\u8eab\u8fb9\u7684\u670b\u53cb\u4eec\u90fd\u7279\u522b\u6f6e\uff0c\u6b7b\u78e8\u786c\u6ce1\u624d\u77e5\u9053[\u53ef\u601c]\u539f\u6765\u5927\u5bb6\u90fd\u5728\u5de5\u5382\u5e97\u4e70\u7684...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>freq</th>\n",
        "      <td>                             8516</td>\n",
        "      <td>                                1</td>\n",
        "      <td>      11620</td>\n",
        "      <td>                                              2891</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "                                     uid                               mid  \\\n",
        "count                             275331                            275331   \n",
        "unique                             24818                            275331   \n",
        "top     875a4a77b339d93f819e2c4de5bd0b57  c2a882d8859f09387be2cfb5717a744c   \n",
        "freq                                8516                                 1   \n",
        "\n",
        "              time                                            context  \n",
        "count       275331                                             275331  \n",
        "unique          32                                             228038  \n",
        "top     2015-01-01  \u8ba9\u4f60\u4ece\u6b64\u6f6e\u8d77\u6765\uff01[\u9177]\u6700\u8fd1\u8eab\u8fb9\u7684\u670b\u53cb\u4eec\u90fd\u7279\u522b\u6f6e\uff0c\u6b7b\u78e8\u786c\u6ce1\u624d\u77e5\u9053[\u53ef\u601c]\u539f\u6765\u5927\u5bb6\u90fd\u5728\u5de5\u5382\u5e97\u4e70\u7684...  \n",
        "freq         11620                                               2891  "
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data[['uid','mid','tick_paramsme','context']].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "\"['tick_paramsme'] not in index\"",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-6-eb4df01e5ce2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweibo_train_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'uid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'tick_paramsme'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'context'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1676\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1677\u001b[0m             \u001b[0;31m# either boolean or fancy integer index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1678\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1679\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1680\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_array\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1720\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1721\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1722\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1723\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/indexing.pyc\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[1;32m   1066\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_setter\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1067\u001b[0m                         \u001b[0;32mreturn\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'key'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1068\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%s not in index'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1069\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1070\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: \"['tick_paramsme'] not in index\""
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data['uid','mid','tick_paramsme','context'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "('uid', 'mid', 'tick_paramsme', 'context')",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-7-ba8abb326e7d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweibo_train_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'uid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'tick_paramsme'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'context'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1682\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1683\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1689\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1691\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1053\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2536\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2537\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2538\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2539\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1154\u001b[0m         \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m         \"\"\"\n\u001b[0;32m-> 1156\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3353)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3233)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11148)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11101)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: ('uid', 'mid', 'tick_paramsme', 'context')"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data['uid'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "count                              1626750\n",
        "unique                               45671\n",
        "top       875a4a77b339d93f819e2c4de5bd0b57\n",
        "freq                                 46873\n",
        "dtype: object"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data['uid','mid'].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "KeyError",
       "evalue": "('uid', 'mid')",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-9-04c4be64218f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweibo_train_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'uid'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'mid'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1682\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1683\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1684\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1685\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1686\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1689\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1691\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1692\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1693\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionaility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/generic.pyc\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1051\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1052\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1053\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1054\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/internals.pyc\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2536\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2537\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2538\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2539\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/core/index.pyc\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1154\u001b[0m         \u001b[0mloc\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0munique\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpossibly\u001b[0m \u001b[0mslice\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mmask\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1155\u001b[0m         \"\"\"\n\u001b[0;32m-> 1156\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_values_from_object\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1158\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseries\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3353)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/index.so\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:3233)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11148)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/hashtable.so\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:11101)\u001b[0;34m()\u001b[0m\n",
        "\u001b[0;31mKeyError\u001b[0m: ('uid', 'mid')"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data[['uid','mid']].describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>uid</th>\n",
        "      <th>mid</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td>                          1626750</td>\n",
        "      <td>                          1626750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>unique</th>\n",
        "      <td>                            45671</td>\n",
        "      <td>                          1626750</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>top</th>\n",
        "      <td> 875a4a77b339d93f819e2c4de5bd0b57</td>\n",
        "      <td> d3cacc46be3868d9c3a7741b6b06d1e6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>freq</th>\n",
        "      <td>                            46873</td>\n",
        "      <td>                                1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "                                     uid                               mid\n",
        "count                            1626750                           1626750\n",
        "unique                             45671                           1626750\n",
        "top     875a4a77b339d93f819e2c4de5bd0b57  d3cacc46be3868d9c3a7741b6b06d1e6\n",
        "freq                               46873                                 1"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.time.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "TypeError",
       "evalue": "cannot concatenate 'str' and 'float' objects",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-11-483e3aebf512>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweibo_train_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/pandas/tools/plotting.pyc\u001b[0m in \u001b[0;36mhist_series\u001b[0;34m(self, by, ax, grid, xlabelsize, xrot, ylabelsize, yrot, figsize, bins, **kwds)\u001b[0m\n\u001b[1;32m   2616\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2618\u001b[0;31m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2619\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2620\u001b[0m         \u001b[0maxes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/matplotlib/axes.pyc\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs)\u001b[0m\n\u001b[1;32m   8324\u001b[0m             \u001b[0;31m# this will automatically overwrite bins,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8325\u001b[0m             \u001b[0;31m# so that each histogram uses the same bins\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8326\u001b[0;31m             \u001b[0mm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistogram\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mhist_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   8327\u001b[0m             \u001b[0mm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# causes problems later if it's an int\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8328\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmlast\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;32m/Users/Pro/canopy/lib/python2.7/site-packages/numpy/lib/function_base.pyc\u001b[0m in \u001b[0;36mhistogram\u001b[0;34m(a, bins, range, normed, weights, density)\u001b[0m\n\u001b[1;32m    174\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                 \u001b[0mrange\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m         \u001b[0mmn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m0.0\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mmi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mmx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mmn\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0;36m0.5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mTypeError\u001b[0m: cannot concatenate 'str' and 'float' objects"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x10d471590>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>uid</th>\n",
        "      <th>mid</th>\n",
        "      <th>time</th>\n",
        "      <th>forward_count</th>\n",
        "      <th>comment_count</th>\n",
        "      <th>like_count</th>\n",
        "      <th>context</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 07fc721342df1a4c1992560b582992f8</td>\n",
        "      <td> 5fefc2488f99aba5782baa43096b76dc</td>\n",
        "      <td> 2014-11-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 7</td>\n",
        "      <td> 2</td>\n",
        "      <td> [\u5410]@\u4eac\u4e1c \u7684\u7528\u6237\u4f53\u9a8c\u592a\u5dee\u4e86....\u7b2c\u4e00\u6b21\u5728\u4eac\u4e1c\u8d2d\u7269\uff0c\u4e70\u4e86\u51e0\u767e\u7684\u4e66....\u771f\u662f\u7ecf\u5386\u4e86\u7f51\u8d2d\u7684...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 07fc721342df1a4c1992560b582992f8</td>\n",
        "      <td> 397d9abe2ebf9ba986fe5ab5a6139e11</td>\n",
        "      <td> 2014-11-04</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 0</td>\n",
        "      <td> #\u6211\u7231\u5c0f\u7c73\u624b\u673a#\u56e0\u4e3a\u5c0f\u7c73\u624b\u673a4\u7684\u5c4f\u5e55\u592a\u7ed9\u529b\u4e86\uff0c5\u82f1\u5bf8\u7684\u9ad8\u8272\u5f69\u9971\u548c\u5ea6\u590f\u666e / JDI\u5c4f\u5e55\uff0c19...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 07fc721342df1a4c1992560b582992f8</td>\n",
        "      <td> a2ae3959e9aa21a47993506e2609a4ec</td>\n",
        "      <td> 2014-11-08</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>              [doge][\u55b5\u55b5]\u5929\u732b\u771f\u662f\u725b....\u5356\u5b9e\u9a8c\u8017\u6750\u7684\u90fd\u8dd1\u5929\u732b\u53bb\u5f00\u5e97\u4e86...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 07fc721342df1a4c1992560b582992f8</td>\n",
        "      <td> fcc63a086522040a271ba6d1fb604734</td>\n",
        "      <td> 2014-11-09</td>\n",
        "      <td> 0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td>                                [doge]\u627e\u5de5\u4f5c\u256e(\u256f\u25bd\u2570)\u256d\u2026\u2026</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 07fc721342df1a4c1992560b582992f8</td>\n",
        "      <td> 6ab0f93ab59cbdaf25f4a023642971a6</td>\n",
        "      <td> 2014-11-11</td>\n",
        "      <td> 0</td>\n",
        "      <td> 5</td>\n",
        "      <td> 1</td>\n",
        "      <td>                                  [doge]\u6210\u529f\u62a2\u4e86\u53f0\u7c734\u7535\u4fe1~</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                                uid                               mid  \\\n",
        "0  07fc721342df1a4c1992560b582992f8  5fefc2488f99aba5782baa43096b76dc   \n",
        "1  07fc721342df1a4c1992560b582992f8  397d9abe2ebf9ba986fe5ab5a6139e11   \n",
        "2  07fc721342df1a4c1992560b582992f8  a2ae3959e9aa21a47993506e2609a4ec   \n",
        "3  07fc721342df1a4c1992560b582992f8  fcc63a086522040a271ba6d1fb604734   \n",
        "4  07fc721342df1a4c1992560b582992f8  6ab0f93ab59cbdaf25f4a023642971a6   \n",
        "\n",
        "         time  forward_count  comment_count  like_count  \\\n",
        "0  2014-11-04              0              7           2   \n",
        "1  2014-11-04              0              3           0   \n",
        "2  2014-11-08              0              0           0   \n",
        "3  2014-11-09              0              6           0   \n",
        "4  2014-11-11              0              5           1   \n",
        "\n",
        "                                             context  \n",
        "0  [\u5410]@\u4eac\u4e1c \u7684\u7528\u6237\u4f53\u9a8c\u592a\u5dee\u4e86....\u7b2c\u4e00\u6b21\u5728\u4eac\u4e1c\u8d2d\u7269\uff0c\u4e70\u4e86\u51e0\u767e\u7684\u4e66....\u771f\u662f\u7ecf\u5386\u4e86\u7f51\u8d2d\u7684...  \n",
        "1  #\u6211\u7231\u5c0f\u7c73\u624b\u673a#\u56e0\u4e3a\u5c0f\u7c73\u624b\u673a4\u7684\u5c4f\u5e55\u592a\u7ed9\u529b\u4e86\uff0c5\u82f1\u5bf8\u7684\u9ad8\u8272\u5f69\u9971\u548c\u5ea6\u590f\u666e / JDI\u5c4f\u5e55\uff0c19...  \n",
        "2               [doge][\u55b5\u55b5]\u5929\u732b\u771f\u662f\u725b....\u5356\u5b9e\u9a8c\u8017\u6750\u7684\u90fd\u8dd1\u5929\u732b\u53bb\u5f00\u5e97\u4e86...  \n",
        "3                                 [doge]\u627e\u5de5\u4f5c\u256e(\u256f\u25bd\u2570)\u256d\u2026\u2026  \n",
        "4                                   [doge]\u6210\u529f\u62a2\u4e86\u53f0\u7c734\u7535\u4fe1~  "
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('time').sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>forward_count</th>\n",
        "      <th>comment_count</th>\n",
        "      <th>like_count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>time</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-07-01</th>\n",
        "      <td>  30531</td>\n",
        "      <td> 17809</td>\n",
        "      <td>  9047</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-02</th>\n",
        "      <td> 106699</td>\n",
        "      <td> 47871</td>\n",
        "      <td> 10688</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-03</th>\n",
        "      <td>  16033</td>\n",
        "      <td> 11117</td>\n",
        "      <td>  7038</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-04</th>\n",
        "      <td>  18571</td>\n",
        "      <td> 11564</td>\n",
        "      <td> 10129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-05</th>\n",
        "      <td>  17169</td>\n",
        "      <td> 12650</td>\n",
        "      <td> 13760</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-06</th>\n",
        "      <td>  13849</td>\n",
        "      <td> 10086</td>\n",
        "      <td> 11689</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-07</th>\n",
        "      <td>  19813</td>\n",
        "      <td> 13489</td>\n",
        "      <td> 10826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-08</th>\n",
        "      <td>  21894</td>\n",
        "      <td> 17397</td>\n",
        "      <td> 15259</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-09</th>\n",
        "      <td>  25774</td>\n",
        "      <td> 14828</td>\n",
        "      <td>  9016</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-10</th>\n",
        "      <td>  21708</td>\n",
        "      <td> 12308</td>\n",
        "      <td> 12161</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-11</th>\n",
        "      <td>  13946</td>\n",
        "      <td> 11714</td>\n",
        "      <td>  9476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-12</th>\n",
        "      <td>  13169</td>\n",
        "      <td>  8732</td>\n",
        "      <td>  8867</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-13</th>\n",
        "      <td>  12651</td>\n",
        "      <td> 12212</td>\n",
        "      <td> 12656</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-14</th>\n",
        "      <td>  18237</td>\n",
        "      <td> 14323</td>\n",
        "      <td> 13898</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-15</th>\n",
        "      <td>  18824</td>\n",
        "      <td> 12487</td>\n",
        "      <td>  9970</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-16</th>\n",
        "      <td>  19618</td>\n",
        "      <td> 10432</td>\n",
        "      <td>  7249</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-17</th>\n",
        "      <td>  16226</td>\n",
        "      <td> 12621</td>\n",
        "      <td>  9075</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-18</th>\n",
        "      <td>  19679</td>\n",
        "      <td> 15302</td>\n",
        "      <td> 14987</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-19</th>\n",
        "      <td>  16439</td>\n",
        "      <td> 10396</td>\n",
        "      <td> 10710</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-20</th>\n",
        "      <td>  13667</td>\n",
        "      <td> 16621</td>\n",
        "      <td> 16092</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-21</th>\n",
        "      <td>  16162</td>\n",
        "      <td> 10350</td>\n",
        "      <td>  9313</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-22</th>\n",
        "      <td>  19276</td>\n",
        "      <td> 12188</td>\n",
        "      <td> 15539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-23</th>\n",
        "      <td>  28783</td>\n",
        "      <td> 14265</td>\n",
        "      <td> 13570</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-24</th>\n",
        "      <td>  18916</td>\n",
        "      <td> 13428</td>\n",
        "      <td> 13932</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-25</th>\n",
        "      <td>  19019</td>\n",
        "      <td> 11030</td>\n",
        "      <td>  9452</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-26</th>\n",
        "      <td>  12655</td>\n",
        "      <td>  7833</td>\n",
        "      <td>  7881</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-27</th>\n",
        "      <td>  14097</td>\n",
        "      <td> 10472</td>\n",
        "      <td> 16118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-28</th>\n",
        "      <td>  14268</td>\n",
        "      <td>  9282</td>\n",
        "      <td>  7179</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-29</th>\n",
        "      <td>  54531</td>\n",
        "      <td> 13667</td>\n",
        "      <td> 15378</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-07-30</th>\n",
        "      <td>  16529</td>\n",
        "      <td> 11197</td>\n",
        "      <td>  9658</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-02</th>\n",
        "      <td>  17256</td>\n",
        "      <td>  9492</td>\n",
        "      <td>  9915</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-03</th>\n",
        "      <td>  17788</td>\n",
        "      <td>  7575</td>\n",
        "      <td>  9190</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-04</th>\n",
        "      <td>  22276</td>\n",
        "      <td> 10513</td>\n",
        "      <td> 11646</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-05</th>\n",
        "      <td>  24139</td>\n",
        "      <td>  9620</td>\n",
        "      <td>  9457</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-06</th>\n",
        "      <td>  17006</td>\n",
        "      <td>  7398</td>\n",
        "      <td> 14872</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-07</th>\n",
        "      <td>  15540</td>\n",
        "      <td>  5640</td>\n",
        "      <td>  8074</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-08</th>\n",
        "      <td>  14829</td>\n",
        "      <td>  7781</td>\n",
        "      <td>  8412</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-09</th>\n",
        "      <td>  19253</td>\n",
        "      <td>  8257</td>\n",
        "      <td> 10390</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-10</th>\n",
        "      <td>  23637</td>\n",
        "      <td>  9136</td>\n",
        "      <td> 10242</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-11</th>\n",
        "      <td>  39485</td>\n",
        "      <td> 10860</td>\n",
        "      <td>  7556</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-12</th>\n",
        "      <td>  17073</td>\n",
        "      <td>  8176</td>\n",
        "      <td>  7028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-13</th>\n",
        "      <td>  14381</td>\n",
        "      <td>  7154</td>\n",
        "      <td>  8712</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-14</th>\n",
        "      <td>  13239</td>\n",
        "      <td>  7134</td>\n",
        "      <td> 12280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-15</th>\n",
        "      <td>  16803</td>\n",
        "      <td>  7247</td>\n",
        "      <td>  8678</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-16</th>\n",
        "      <td>  17716</td>\n",
        "      <td> 11772</td>\n",
        "      <td> 12374</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-17</th>\n",
        "      <td>  17404</td>\n",
        "      <td>  7453</td>\n",
        "      <td>  8830</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-18</th>\n",
        "      <td>  19092</td>\n",
        "      <td>  7586</td>\n",
        "      <td>  8859</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-19</th>\n",
        "      <td>  15517</td>\n",
        "      <td>  7004</td>\n",
        "      <td>  7604</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-20</th>\n",
        "      <td>  18634</td>\n",
        "      <td>  7520</td>\n",
        "      <td>  8882</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-21</th>\n",
        "      <td>  10341</td>\n",
        "      <td>  5197</td>\n",
        "      <td>  7652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-22</th>\n",
        "      <td>  15090</td>\n",
        "      <td>  6827</td>\n",
        "      <td>  7529</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-23</th>\n",
        "      <td>  15902</td>\n",
        "      <td>  7681</td>\n",
        "      <td>  8776</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-24</th>\n",
        "      <td>  22210</td>\n",
        "      <td>  9820</td>\n",
        "      <td> 11605</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-25</th>\n",
        "      <td>  19233</td>\n",
        "      <td> 10614</td>\n",
        "      <td> 11652</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-26</th>\n",
        "      <td>  18410</td>\n",
        "      <td>  6941</td>\n",
        "      <td>  9867</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-27</th>\n",
        "      <td>  23162</td>\n",
        "      <td>  7622</td>\n",
        "      <td> 11716</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-28</th>\n",
        "      <td>  15478</td>\n",
        "      <td>  6632</td>\n",
        "      <td>  9044</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-29</th>\n",
        "      <td>  29055</td>\n",
        "      <td>  8760</td>\n",
        "      <td> 10429</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-30</th>\n",
        "      <td>  18960</td>\n",
        "      <td>  8565</td>\n",
        "      <td> 11054</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-12-31</th>\n",
        "      <td>  14528</td>\n",
        "      <td>  8969</td>\n",
        "      <td> 11214</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>184 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "            forward_count  comment_count  like_count\n",
        "time                                                \n",
        "2014-07-01          30531          17809        9047\n",
        "2014-07-02         106699          47871       10688\n",
        "2014-07-03          16033          11117        7038\n",
        "2014-07-04          18571          11564       10129\n",
        "2014-07-05          17169          12650       13760\n",
        "2014-07-06          13849          10086       11689\n",
        "2014-07-07          19813          13489       10826\n",
        "2014-07-08          21894          17397       15259\n",
        "2014-07-09          25774          14828        9016\n",
        "2014-07-10          21708          12308       12161\n",
        "2014-07-11          13946          11714        9476\n",
        "2014-07-12          13169           8732        8867\n",
        "2014-07-13          12651          12212       12656\n",
        "2014-07-14          18237          14323       13898\n",
        "2014-07-15          18824          12487        9970\n",
        "2014-07-16          19618          10432        7249\n",
        "2014-07-17          16226          12621        9075\n",
        "2014-07-18          19679          15302       14987\n",
        "2014-07-19          16439          10396       10710\n",
        "2014-07-20          13667          16621       16092\n",
        "2014-07-21          16162          10350        9313\n",
        "2014-07-22          19276          12188       15539\n",
        "2014-07-23          28783          14265       13570\n",
        "2014-07-24          18916          13428       13932\n",
        "2014-07-25          19019          11030        9452\n",
        "2014-07-26          12655           7833        7881\n",
        "2014-07-27          14097          10472       16118\n",
        "2014-07-28          14268           9282        7179\n",
        "2014-07-29          54531          13667       15378\n",
        "2014-07-30          16529          11197        9658\n",
        "...                   ...            ...         ...\n",
        "2014-12-02          17256           9492        9915\n",
        "2014-12-03          17788           7575        9190\n",
        "2014-12-04          22276          10513       11646\n",
        "2014-12-05          24139           9620        9457\n",
        "2014-12-06          17006           7398       14872\n",
        "2014-12-07          15540           5640        8074\n",
        "2014-12-08          14829           7781        8412\n",
        "2014-12-09          19253           8257       10390\n",
        "2014-12-10          23637           9136       10242\n",
        "2014-12-11          39485          10860        7556\n",
        "2014-12-12          17073           8176        7028\n",
        "2014-12-13          14381           7154        8712\n",
        "2014-12-14          13239           7134       12280\n",
        "2014-12-15          16803           7247        8678\n",
        "2014-12-16          17716          11772       12374\n",
        "2014-12-17          17404           7453        8830\n",
        "2014-12-18          19092           7586        8859\n",
        "2014-12-19          15517           7004        7604\n",
        "2014-12-20          18634           7520        8882\n",
        "2014-12-21          10341           5197        7652\n",
        "2014-12-22          15090           6827        7529\n",
        "2014-12-23          15902           7681        8776\n",
        "2014-12-24          22210           9820       11605\n",
        "2014-12-25          19233          10614       11652\n",
        "2014-12-26          18410           6941        9867\n",
        "2014-12-27          23162           7622       11716\n",
        "2014-12-28          15478           6632        9044\n",
        "2014-12-29          29055           8760       10429\n",
        "2014-12-30          18960           8565       11054\n",
        "2014-12-31          14528           8969       11214\n",
        "\n",
        "[184 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('time').sum().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10dbb4650>"
       ]
      },
      {
       "metadata": {},
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GK95o1pZC4xExGKMtBQ9iBPs0nkGXZ/PgFaKgLYXGo2MKGo0GvEQUtKXQeIz3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\nRCKRSCQSiaTE+X8i3KgDWEeMqQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10dbbd790>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'fw' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[0;32m<ipython-input-1-9d8e3a73d6f5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfw\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweibo_train_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[0;31mNameError\u001b[0m: name 'fw' is not defined"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import weiboPredict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from weiboPredict import framework as fw"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.loadData()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('uid').sum().comment_count.hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10f4a8490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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85nrgeBzbAtzzAc5DktQj3RSSfwN+MmnsOmBb3N4G3BC3rwceA94GxoCDwBXAEuB8YE88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\nKTZD8wlXLAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10b51ed10>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('uid').sum().comment_count.hist?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Object `comment_count.hist` not found.\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('uid').sum().comment_count.hist()?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "SyntaxError",
       "evalue": "invalid syntax (<ipython-input-15-b96e7f846779>, line 1)",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-15-b96e7f846779>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    fw.weibo_train_data.groupby('uid').sum().comment_count.hist()?\u001b[0m\n\u001b[0m                                                                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, Series"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Series.hist?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('uid').sum().comment_count.hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x10f49a650>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10f494550>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data.groupby('uid').sum().forward_count.hist(bins=200,xrot=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 26,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x11bdc0a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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P+HRa50LgIWA88Czw7mEy2DORpFEqvWcyiSgkpO+T0vJkYGVlvZXAPsOMr0rjpO8r0vIG\nYB0xjSZJ6hDjt8JtDKavMbd+/QssXLgQgJ6eHnp7e+nr6wOa85WtvLx48WLOO++8tv3+4S43xkrJ\nU81SSp7GZR+/zn38Lr300ra//odeLuX5NDAwQH9/PwDTpk2jNNPYuAG/lJjeAtg7XYboncyvrHcH\ncFRa94nK+JlED6WxztFpeTzw/AgZBidOnDpYkkWLFrU7wibMlK/EXGbKY6Z8tGhnf0t7Jl8jmuYX\nE8WjJ32fCfwFcCQxfXUPcCBxZx4CzgEeBr4LXE4Uknnpdj9D9FJOSd+HsmciSaPUqp5JzjTX9USz\nfU+it/FF4CLgRuJMrOXAaWndJWl8CdH/mEezKs4D+oEdgduJQgJwNXAdcWrwiwxfSCRJBctpwJ9J\nNNAnAFOIU4VfAo4nTg0+AXi5sv5XiKORGcCdlfFHiSOQA4kjlIbXiWI0nZjuWj76u9Ee1bnkUpgp\nX4m5zJTHTOXxE/CSpNr821yS1MVK/5yJJEn/zmJSQ4lzpGbKV2IuM+UxU3ksJpKk2uyZSFIXs2ci\nSeoYFpMaSpwjNVO+EnOZKY+ZymMxkSTVZs9EkrqYPRNJUsewmNRQ4hypmfKVmMtMecxUHouJJKk2\neyaS1MXsmUiSOobFpIYS50jNlK/EXGbKY6byWEwkSbXZM5GkLmbPRJLUMSwmNZQ4R2qmfCXmMlMe\nM5XHYiJJqs2eiSR1MXsmkqSOYTGpocQ5UjPlKzGXmfKYqTwWE0lSbfZMJKmL2TORJHUMi0kNJc6R\nmilfibnMlMdM5bGYSJJqs2ciSV3MnokkqWNYTGoocY7UTPlKzGWmPGYqj8VEklSbPRNJ6mL2TCRJ\nHaOkYjIbWAosAy5oc5YsJc6RmilfibnMlMdM5SmlmGwL/G+ioMwEzgQOaWuiDIsXL253hE2YKV+J\nucyUx0zlKaWYHAk8BSwH3gS+A5zczkA5Xn755XZH2ISZ8pWYy0x5zFSeUorJPsCKyuWVaUyS1AFK\nKSaDOSu99NIaxo0bx7hxE9LXyMu77rrHZm9v1133yFpvJMuXL9/i644VM+UrMZeZ8pipPKWcGnw0\nsJDomQAsAN4GLq6s8xRwQGtjSVLHexo4sN0hWmU8cYenAROAxXRAA16SVJ5fBf6FOAJZ0OYskiRJ\nklpp23YHyHAA8Eni8ycrgJ+2N44kaahSzuYayV7AhcARwHHAF9sbZ4v9GnA98D3g4DZnGWoScaLD\nzTRPgGi3EjMBzABuB/4JmNvmLA0lZoIyc5Wa6btEprPbnKWhxEy1fRz427S8L/At4PT2xdms04Bv\nAl8Adk5jOwE3AOcAvwvcCBzWhmwzgfnA5cB7KuNfBb5OfEj0B8D73+GZYNPi3zjr8RricdwHeByY\n9Q7PVGquTsp0bco0udMzlX5ksg3Q+DPBLwL/QHxavp12AE4CLiOOlsan8SOA3wYeIyr7OcQ04vHA\nS8BVQD9xyvOvj2G+9wKfBX6nkg3ipIb9gdeBPwYOSuN9xJPnVuCmlG3yOyATjFz85xDPtRuAL9Ms\n/lOA7wOriMfzI8B+74BMpebqtEzfHybTvml8dadnKr2YbAB+RryB/wx4DtgF2LUFv3tnohB8lo03\n5G8B84B1wH8Hzkrjc4B/Jfao/yfxgBwLbE8UlZ+n9RYBv7IV8v0S8J/Y+BTqKcBXiM/tnAn8lzQ+\nC3gj5f0DYE/gY8TRwCPEEwvgAWA34hTtbsm0ueK/mI2L/yw2Lv6DwEeJvdkfAe9K1/+ntDy1SzKV\nmqsTMw3doWxkuprmDmXXZSq9mKwkPncyJV1+A3gN6NmKv2M7YhplDlG9T0zjnwP+iNhLPhd4Xxo/\nlphP/GL6/hGiYPwrzSL3NPAM8B+I050PJh4siAen8QGinA+NTgI+DFQ/qn8iccj+CeBLxJMI4L8R\nf3n5LOIN/HDgqHTdN9IXREE7nNi2b9LcnquJF9+7N5PpPcQb/3E03/TbnSm3+P9OGm8U/8vZuPjv\nwKbF/zDitbKB5uPwbMr1i/6Ewp7EC3Q+UWR3KSATuK1yM22tHcqhmQaIHcqx3E6tzASUX0yWEXf4\nQ+nydKLi/ngr/o7fJ/Z+e4EngR8S0zIHEIeHv01U8E8RhecN4rMwEP2ct4litILmA/tT4o9WTk/r\n7kycTADwBM2/O1b9MzI7EXsRew+T7U+A84gpIYhe0m1Ec/r7NPtIa2gWtCVEUetLGWYQb9IQe/7T\ngZ8Qxbmx1/GTdP11QzI1ppiOBO4H/h74H8SRSMN/bFGmCcT2nke8YCam8fOIItaXlkcq/rOIF9LT\nbFr8P0QUvhlsXPwPIY6KX6H5Vxh+QjyOq4ZkmpNu/wPAPcBFRCFcQxxdk37PWGdqbKsPAL9H7JE2\ndsrava0amc4Ddid2Itq9rUjb4SLiOQ8b71D+ou300S3INKOSqfE6GinTxWn9w4B7M7fTWGYaVunF\n5DXizes3iPm+2cCDW/l3LCP2Xj5HPEjPEk/4XyaOjCDm7j9GvPHtRBQViKL2FlG1f0zzDXCQKChv\npO+P0txTmJF+326VDPsTb8D/QBQQiBfAe4lidmT6nZ+muefwZFrvnpThyJShcaTwKlHQDibeuHto\n7l08nsZfAx4CTk3jzxNPwh+NkGkboiD8Z+Kx+Fa6f/uk+zrWmSDehL6Rts1sYg/tV8gv/m8RbxYr\n2bT4H0i84N5Fs/gvIabYXk/3q/E4vpx+71NDMp1I/GmgDWn9TxCF9740tl+6rbHOBFHYriR2lA4n\n3hCPIx7bdm2raqb3EY/fbsCdbdxWjffB2cRj2UvswR/cou30iYxMRxNv7APE0dlYb6fhMv1CpRcT\niDesbxB7x39GnC66Nf2A2FhnEW8C7yfeuKp/B+xx4vByG+IBnURU/deAHYnpqkfSOu8jHsgTiKMc\ngP+Vfsd9xNlcf05zTxuigH2CeJM+NI3tQhS0xm3cREwdvU08Cd5K488QRW4SMSXUOIoYJF4A2wLr\niem2U9LP9kz3ew/gb4C1xJ/9f4Q4u+PVIZkaPZDV6We96evoNP56yjvWmcYRp3aeAHyGaNpPIfbY\nZtD8y9O3MHzxX8HIxf9nbFz8Gy+mg4kXcA/wd8QL7f50/StTrmqmLxEnEjSK5CfT12+ly+uJx2+s\nM0FM274/5fo88eayQ9pe7dpW1UzzgWOAF4ip4t9t07Z6m3iDnUkcBRxEPEcPpblDuSXb6afEa2No\nphkp0+4p090p04oRMn2VeMzWEu9BZ9fYTnUy/ULjN7dCATYAd6WvsbCCeFM7nqi+lxN7cK8TFb9x\nNtmadPlviTfAv0lj09LPB4kzkD5HvLHuSRRC0u1/mdjb+Rc2/nP7EPOWPySeEPumsWVs/JmUx2i+\nKb9KHOZuRzyRtiMeyx8QexrT0/WPJfY4qNyvI4m91EuJN3aIF/ZxxF7I36f7TiVTY3pkLfEiO5/Y\nw1lHHLVdm+5DKzI9XdkmzxJF7dx0Ww0jFf9XieIP8ULaM11/MfFGUS3+XyCmFKYRU0SNf1bxBWLP\nfnW67aGZ1hA7Af+Y8h9LTDPsThTlL6ft854xzgTxhjKBOPHhROJN4n42/qN/rd5WDMl0f8rwStpW\nu7VpWx2QLv8j8dyrvra3dDudkK43UqbG7//iZjI9QvRt3kiX62ynupm0GTtVls8n3pweofnBpt2J\n0+cac6n/lygU16Svap/j+HS9LfkLx3sRD3RjCuw5msUFothNJY6iLqX52YwHgN9My18n/mvlHxJ7\nGsdUrn8McXh8Ehvf55xMjXnX/WmeOPAh4sW3d4szNX7/gnTbEHO6+1fWeSJdPpl4rBqH898nek4Q\ne8bXAn9F7CT0Vq4/g3gsp5CnmukyoujuXvn5dOI5NbWFmRr+D/HYLCJOfljLxg3lVm+raqb7iEJQ\nPcGiHdvqJqLI7UcchUDs1bfzOVXNdDPxHKueydrO55QyfIo46+FM4vDuC8S01Jcq6+xFNFsvIB7Q\nrWUCsfd4XLp8L83D0B2IJ0NjaukviT2STxOFbkYa3444BP5Toim+/VbK1DgJojo1ug1xxNj4UFOr\nMkFs9wHijDmII6R5ablVxX+kTMN9Fmpn4sNhH0mX/6xFmapnDJ6Xvh6l9TtKI2X6feBrbLzT1Opt\nNQP4C+K1fzlxdPwd4gj6k2mdVm+noZmeI3bQDqqs067n1LBK+X8m7TSOmD46mnhCn0g8iH8OfBD4\nr8QRwY3ENM1Yu4qYr7yBOPXvo8TZGYcSRywLiV7NQSnb/sRe3lhNAzYy3U7sHe1AHEK/i2imvofo\nXTzXokzjiCnFS4gptP40PpuYflxFTA8+Q+wIQBT/3ySmH29m6z+OI2XqIQrMXsT89mri7KCftyDT\ncD5FTHE9RuywrKb122qoTxKvva8SZ+ZNpvXbqpfolWwgXuuHEm/KS4li9zTx5t7K7TQ00wzibMlv\nEDt2uxMnqbT7OfXvLCbhV4lzsx8lzha7i/b8QcmJxN77+4mC9pdEE+1LxJvkN4g55nZl+ibx5D2D\nONx+gig0f9fiTKcSUyT/j3hRbSCK7J7EFOUyWlf8h8s0kZhnfoA4geENYjrwFjLOitmKtiUK+2HE\nNMYs4mSG64k38N+jtTtKw2U6ntijXkpsv3XE67DV26rqj4icf0h7diiH82XiPeka4jW3jpjiaud2\n2ojFpBw7Ekcg2xCNsoeAvybjLIoWZfoB0Qi/h3ijzPpXy2PkM8SZPzcTRfYh2vcibxia6UHKeJH/\nBlE0FhOned9N+//ydjXTg8AdND881y6N6dvxRJH7GbHj1E5DM71GnBggSZIkSZIkSZIkSZIkSZIk\nSZIkFev/A7kwrfQLyfyNAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1202ada50>"
       ]
      }
     ],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Series.filter?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=Series([1,2,3])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a>2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "0    False\n",
        "1    False\n",
        "2     True\n",
        "dtype: bool"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(a>2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "pandas.core.series.Series"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b=DataFrame({'a':[1,2,3],'b':[4,1,6],'c':[5,2,4]})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "   a  b  c\n",
        "0  1  4  5\n",
        "1  2  1  2\n",
        "2  3  6  4"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b>2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> False</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "      <td> False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "      <td>  True</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "       a      b      c\n",
        "0  False   True   True\n",
        "1  False  False  False\n",
        "2   True   True   True"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b[b>2]=3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "b"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>a</th>\n",
        "      <th>b</th>\n",
        "      <th>c</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 40,
       "text": [
        "   a  b  c\n",
        "0  1  3  3\n",
        "1  2  1  2\n",
        "2  3  3  3"
       ]
      }
     ],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum=fw.weibo_train_data.groupby('uid').sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum[weibo_train_data_u_sum<50000].hist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x12e687f50>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x12ce0db90>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x12ce317d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x128e17bd0>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y/DW3fjPW4RiYhp19bXbLpeuD98wAtrh9mgC8vv9uLEA5zNN/rhzmZbVlmVcO8/yW+az+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\nq32S2REcAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12e66ee90>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum[weibo_train_data_u_sum<10000].hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x130366f90>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x12dc9fd50>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x12dcc6990>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x131749d90>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nrvHcrvwjlmhX+Vpb70NCCJEkjSqOWmsHL8KSyyZjkT9XJXJ14kdYT63ydXSW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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10fcbd0d0>"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum[weibo_train_data_u_sum<2000].hist(bins=100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "array([[<matplotlib.axes.AxesSubplot object at 0x10fd553d0>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x1303c76d0>],\n",
        "       [<matplotlib.axes.AxesSubplot object at 0x13f202e90>,\n",
        "        <matplotlib.axes.AxesSubplot object at 0x13f2252d0>]], dtype=object)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/0YapUy4v8fo/YyOQCeHYOVj42ovAj4B9KviM44H/BLYATwPTQ/neWHjf85hL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\nIYQQQgghhBBCCCGEEEIIIYRoZf4/Jr1CvLq7uUoAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1106ad4d0>"
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum.forward_count[weibo_train_data_u_sum.forward_count<100].hist(bins=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x1449e9050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x143d7fbd0>"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum.comment_count[weibo_train_data_u_sum.comment_count<100].hist(bins=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 52,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x14498a490>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x144a5b4d0>"
       ]
      }
     ],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum.like_count[weibo_train_data_u_sum.like_count<100].hist(bins=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x14499ccd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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Yi8RY5MMEIUlqyxpEZtYgJJWPNQhJUu5MECXk+mpiLBJjkRiLfGRJEF8H9gPbmvpWAIPA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\nodppAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x144aa0ad0>"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u770b\u4e00\u4e0b\u7528\u6237\u7684\u8f6c\u53d1\u4e0e\u8bc4\u8bba\u95f4\u7684\u5173\u7cfb"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.scatter(weibo_train_data_u_sum.forward_count,weibo_train_data_u_sum.comment_count)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 56,
       "text": [
        "<matplotlib.collections.PathCollection at 0x144eede90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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0ws+wVPoHIWAgDGP9hTCvUkr9S8zjhF/2Jvm6ufMest8mcBPtY+K/nNDfwwn9\nb28T//DhOZmfkx37vYoPTzwW0026KcL9YNOb1ZdCHw/JOV8l8CThi2lLoW/NjSL7QbZU+teF8IXD\nAF2BvxJWjJVK/xLxNcL4307gbbL/dQDk5+bO3CXMd9N+ljCfRVjVtJYwedeezAbeBOoJP7sJhF/A\nR2l5CWWx3aR7GmFIaTnZ/zdpDKXRx+OAZYS+vUCYu4DS6Ftzo8iuLiuV/h1B+NktJyyvb/q3oVT6\nJ0mSJEmSJEmSJEmSJEmSJEmSJEmSdGD4/+Qmzf7NHZieAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x144ec1710>"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u627e\u5230\u90a3\u4e2a\u8bc4\u8bba\u5f88\u591a\u8f6c\u53d1\u5f88\u5c11\u7684\u5f02\u5e38\u70b9"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weibo_train_data_u_sum[weibo_train_data_u_sum.comment_count>200000]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>forward_count</th>\n",
        "      <th>comment_count</th>\n",
        "      <th>like_count</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>uid</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>4ac74188244820225e905a3cf93a83f5</th>\n",
        "      <td> 85013</td>\n",
        "      <td> 216480</td>\n",
        "      <td> 386849</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "                                  forward_count  comment_count  like_count\n",
        "uid                                                                       \n",
        "4ac74188244820225e905a3cf93a83f5          85013         216480      386849"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fw.weibo_train_data[fw.weibo_train_data.uid=='4ac74188244820225e905a3cf93a83f5'].groupby('time').sum().plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x144ef07d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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8e0KIolm/mjGlYqXvDdG+ezUEv2tvby/ZbBbXdUM/q1mIbZ8GIDySL7V9kps3t6lF9aFT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g/HjxMePiRsH5ThE3Rhlq9h4I7IPjOlyZr/n3/8z7nvzH3861f862zd4qIgzGeTB4nD04cLFg+r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\n5Z9v6lQYeOVHjPCYUzWfdJK+N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qmjZx5IYy1YsGDBggULFixYsGDBggULFixYsGDBggUL\nFixYsGDBggULFixYsGDBggULFixYsGDBggULFixYsGDBggULnyP8P6D3a7GcpQh1AAAAAElFTkSu\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x144eede50>"
       ]
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}